Coefficient of Performance Prediction Model for an On-Site Vapor Compression Refrigeration System Using Artificial Neural Network

Abstract

Refrigeration system is essential in ensuring the comfort of people, preserving food for extended periods, and supporting the functionality of technological devices. However, refrigeration system accounts for approximately 17% of global electricity consumption due to the substantial energy requirement of compression work. This high consumption rate shows the need to reduce operational and maintenance costs by monitoring the efficiency of refrigeration system using Coefficient of Performance (COP). Currently, there are two methods of monitoring COP, namely substituting actual values into theoretical formulas, and developing artificial intelligence model for COP values. Therefore, this study aimed to develop COP prediction model using Artificial Neural Network (ANN) at a room set point temperature of -25°C. The results showed that through the analysis of ANN parameters, prediction model was successfully developed with an RMSE of 0.0621, an R2 value of 0.8162, and a training speed of 27.3 seconds. The developed prediction model had a CvRMSE value of 3.41 and an MBe of 0.14 which falls within the acceptable values. The prediction model was able to predict COP values of other CDUs, with the same specification, for set point temperature of -21°C. This study showed a promising strategy for monitoring COP of an on-site vapor compression refrigeration system using a data-driven method.

Keywords

• artificial neural network
• coefficient of performance
• machine learning
• refrigeration
• vapor compression system